# Dataset Partition Sizes — Category Frequencies + Date by Category Pivot

> Dataset Partition Sizes — Category Frequencies + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Analyzes dataset partition size records (row_count or file_mb) to produce two reports: category frequency counts and a date-by-category pivot table.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-110?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-partition-sizes-category-frequencies-date-by-category-pivot-bd34c98b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8t4z1JSBohEqjndtzCPxs

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability dataset-partition-sizes-category-frequencies-date-by-category-pivot-bd34c98b -d '<json body>'
```

Example prompt: Can you run two analytics reports on my dataset partition records — give me the category frequencies and a date-by-category pivot for the row_count field? Here are my rows: group-a on 2026-10-01 with 10 rows and 1 MB, group-b on 2026-10-02 with 20 rows and 2 MB, group-a on 2026-10-03 with 15 rows and 3 MB, group-b on 2026-10-04 with 30 rows and 4 MB.

## When to prefer this

Choose this endpoint when you have a flat list of dataset partition records and need both a quick category frequency count and a date-by-category pivot in a single call for under $0.01. It is ideal for lightweight, local analytical summaries on up to 200 rows without needing a full BI tool or external query engine. Prefer alternatives if you need more than 200 rows, live data lookups, trend forecasting, or reports beyond frequency and pivot.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, row_count, file_mb) cause row-level validation errors
- Date strings not matching YYYY-MM-DD pattern will fail schema validation
- Null numeric values for the selected field may produce incomplete pivot cells
- Invalid field enum value (not 'row_count' or 'file_mb') causes request rejection

## How this service works

Dataset partition sizes — Category frequencies + Date by category pivot: Category frequencies + Date by category pivot for supplied dataset partition sizes records (row_count, file_mb). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns two independent analytical reports: (1) a category frequency table showing how often each dataset_group value appears in the supplied rows, and (2) a date-by-category pivot table showing the selected numeric field (row_count or file_mb) broken down by date and dataset_group. Covers up to 200 input rows per call.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "row_count",
     "file_mb"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "file_mb": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "row_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "file_mb": 1,
     "row_count": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "file_mb": 2,
     "row_count": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "file_mb": 3,
     "row_count": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "file_mb": 4,
     "row_count": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "row_count",
    "file_mb"
   ],
   "type": "string",
   "default": "row_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-category-frequencies-date-by-category-pivot-bd34c98b/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)